For example, a second-order GCN was proposed in [1]} by modeling spatial-spectral relations on manifolds for HSIC by the attempts to reduce the computational cost on graphs. Authors of [2]} first used superpixel segmentation techniques on HSIs and fed superpixels instead of pixels into GCNs. This enables the network training of GCNs on a large number of pixels in HSIs with the application to the land cover classification task. Nevertheless, these methods still fail to solve the problem of GCNs essentially. To this end, Hong et al. [3]} proposed a novel miniGCN. As the name suggests, miniGCN trains the GCNs in a mini-batch fashion, which is the same as CNN. The proposed miniGCN not only reduces the computational cost-effectively but also makes it possible to make a quantitative comparison and fusion with CNNs, further yielding a FuNet for HSIC.
